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A Trained-once Crowd Counting Method Using Differential WiFi Channel State Information 基于差分WiFi信道状态信息的训练一次人群计数方法
Workshop on Physical Analytics Pub Date : 2016-06-26 DOI: 10.1145/2935651.2935657
S. D. Domenico, M. Sanctis, E. Cianca, G. Bianchi
{"title":"A Trained-once Crowd Counting Method Using Differential WiFi Channel State Information","authors":"S. D. Domenico, M. Sanctis, E. Cianca, G. Bianchi","doi":"10.1145/2935651.2935657","DOIUrl":"https://doi.org/10.1145/2935651.2935657","url":null,"abstract":"This paper focuses on the problem of providing a rough count of the number of people in a room using passive WiFi Channel State Information (CSI) measurements taken by a single commodity receiver. The feature which mainly distinguishes our work from others is the attempt to emerge with an approach which does not require any dedicated training inside the specific environment where the system is deployed. Our proposal stems from the intuitive observation that features which account for em variations of CSI are expected to be less sensitive to the surrounding environment as opposed to features which account for absolute CSI measurements. We turn such intuition into a concrete proposal, by suitably identifying a set of differential CSI feature candidates, and by selecting the (two) most effective ones via minimization of the summation of the Davies-Bouldin indexes. We preliminary assess the effectiveness of the proposed approach by training once for all the system in a room, and testing the system in two em different rooms having different size and furniture, and involving people freely moving in the rooms with no a-priori movement constraints.","PeriodicalId":139697,"journal":{"name":"Workshop on Physical Analytics","volume":"129 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122170479","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 54
MobiCamp: a Campus-wide Testbed for Studying Mobile Physical Activities MobiCamp:一个校园范围内研究移动体育活动的测试平台
Workshop on Physical Analytics Pub Date : 2016-06-26 DOI: 10.1145/2935651.2935654
Mengyu Zhou, Kaixin Sui, Minghua Ma, Youjian Zhao, Dan Pei, T. Moscibroda
{"title":"MobiCamp: a Campus-wide Testbed for Studying Mobile Physical Activities","authors":"Mengyu Zhou, Kaixin Sui, Minghua Ma, Youjian Zhao, Dan Pei, T. Moscibroda","doi":"10.1145/2935651.2935654","DOIUrl":"https://doi.org/10.1145/2935651.2935654","url":null,"abstract":"Ubiquitous WiFi infrastructure and smart phones offer a great opportunity to study physical activities. In this paper, we present MobiCamp, a large-scale testbed for studying mobility-related activities of residents on a campus. MobiCamp consists of ~2,700 APs, ~95,000 smart phones, and an App with ~2,300 opt-in volunteer users. More specifically, we capture how mobile users interact with different types of buildings, with other users, and with classroom courses, etc. To achieve this goal, we first obtain a relatively complete coverage of the users' mobility traces by utilizing four types of information from SNMP and by relaxing the location granularity to roughly at the room level. Then the popular App provides user attributes (grade, gender, etc.) and fine-grained behavior information (phone usages, course timetables, etc.) of the sampled population. These detailed mobile data is then correlated with the mobility traces from the SNMP to estimate the entire campus population's physical activities. We use two applications to show the power of MobiCamp.","PeriodicalId":139697,"journal":{"name":"Workshop on Physical Analytics","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123131756","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 14
Fusing WiFi and Video Sensing for Accurate Group Detection in Indoor Spaces 融合WiFi和视频传感，实现室内空间精准群体检测
Workshop on Physical Analytics Pub Date : 2016-06-26 DOI: 10.1145/2935651.2935659
Kasthuri Jayarajah, Zaman Lantra, Archan Misra
{"title":"Fusing WiFi and Video Sensing for Accurate Group Detection in Indoor Spaces","authors":"Kasthuri Jayarajah, Zaman Lantra, Archan Misra","doi":"10.1145/2935651.2935659","DOIUrl":"https://doi.org/10.1145/2935651.2935659","url":null,"abstract":"Understanding one's group context in indoor spaces is useful for many reasons -- e.g., at a shopping mall, knowing a customer's group context can help in offering context-specific incentives, or estimating taxi demand for customers exiting the mall. Group detection and monitoring using WiFi-based indoor location traces fails when users are invisible (either because they don't carry smartphones, or because their WiFi is turned OFF) or when location tracking is inaccurate. In this paper, we propose a multi-modal group detection system that fuses two independent modes: video and WiFi, for detecting groups with low latency and high accuracy. We present preliminary results from a micro-study with 20 group episodes and report an overall precision of 0.81 and recall of 0.9, an improvement of over ≈20% over WiFi-based group detection.","PeriodicalId":139697,"journal":{"name":"Workshop on Physical Analytics","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126423133","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 6
Next Generation Physical Analytics for Digital Signage 数字标牌的下一代物理分析
Workshop on Physical Analytics Pub Date : 2016-06-26 DOI: 10.1145/2935651.2935658
Mateusz Mikusz, A. Noulas, N. Davies, S. Clinch, A. Friday
{"title":"Next Generation Physical Analytics for Digital Signage","authors":"Mateusz Mikusz, A. Noulas, N. Davies, S. Clinch, A. Friday","doi":"10.1145/2935651.2935658","DOIUrl":"https://doi.org/10.1145/2935651.2935658","url":null,"abstract":"Traditional digital signage analytics are based on a display-centric view of the world, reporting data on the content shown augmented with frequency of views and possibly classification of the audience demographics. What these systems are unable to provide, are insights into viewers' overall experience of content. This is problematic if we want to understand where, for example, to place content in a network of physically distributed digital signs to optimise content exposure. In this paper we propose a new approach that combines mobility simulations with comprehensive signage analytics data to provide viewer-centric physical analytics. Our approach enables us to ask questions of the analytics from the viewer's perspective for the first time, including estimating the exposure of different user groups to specific content across the entire signage network. We describe a proof of concept implementation that demonstrates the feasibility of our approach, and provide an overview of potential applications and analytics reports.","PeriodicalId":139697,"journal":{"name":"Workshop on Physical Analytics","volume":"520 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131836945","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 11
Margdarshak: A Mobile Data Analytics based Commute Time Estimator cum Route Recommender Margdarshak:一个基于移动数据分析的通勤时间估计器和路线推荐器
Workshop on Physical Analytics Pub Date : 2016-06-26 DOI: 10.1145/2935651.2935655
Rohit Verma, Aviral Shrivastava, Sandip Chakraborty, Bivas Mitra
{"title":"Margdarshak: A Mobile Data Analytics based Commute Time Estimator cum Route Recommender","authors":"Rohit Verma, Aviral Shrivastava, Sandip Chakraborty, Bivas Mitra","doi":"10.1145/2935651.2935655","DOIUrl":"https://doi.org/10.1145/2935651.2935655","url":null,"abstract":"Waiting at traffic signals and getting stuck in traffic congestion eats a lot of time for a commuter in most of the metro cities of the world. Although there exists a large pool of navigation applications, but all of them turn out to be ineffective for dynamically finding out the best route under uncertainty. In this work, we present Margdarshak, a navigation system which utilizes the impact of congestion and wait time at traffic signals for estimating the travel time over a route. We collected a month-long traffic data from different routes at five various cities in India for analyzing the problem in detail. The evaluations performed over the system show that Margdarshak gives a mean estimation error of ±1.5$ minutes, and performs significantly better under uncertainty, compared to other state of the art navigation systems like Google Maps, Here Maps and Waze.","PeriodicalId":139697,"journal":{"name":"Workshop on Physical Analytics","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130965698","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 2
Capturing Personal and Crowd Behavior with Wi-Fi Analytics 捕捉个人和人群的行为与Wi-Fi分析
Workshop on Physical Analytics Pub Date : 2016-06-26 DOI: 10.1145/2935651.2935656
Utku Günay Acer, G. Vanderhulst, A. Mashhadi, Aidan Boran, Claudio Forlivesi, P. Scholl, F. Kawsar
{"title":"Capturing Personal and Crowd Behavior with Wi-Fi Analytics","authors":"Utku Günay Acer, G. Vanderhulst, A. Mashhadi, Aidan Boran, Claudio Forlivesi, P. Scholl, F. Kawsar","doi":"10.1145/2935651.2935656","DOIUrl":"https://doi.org/10.1145/2935651.2935656","url":null,"abstract":"We present a solution for analysing crowds at events such as conferences where people have networking opportunities. Often, potential social relations go unexploited because no business cards were exchanged or we forget about interesting people we met earlier. We created a solution built on top of ubiquitous Wi-Fi signals that is able to create a memory of human trajectories and touch points. In this paper we elaborate on the technological assets we designed to perform crowd anlaytics. We present small wearable Wi-Fi badges that last for the duration of an event (up to 3 days) with a single charge, as well as network equipment that senses the signals radiating from these badges and contemporary mobile devices.","PeriodicalId":139697,"journal":{"name":"Workshop on Physical Analytics","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129497649","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 13
Small Scale Deployment of Seat Occupancy Detectors 座位占用探测器的小规模部署
Workshop on Physical Analytics Pub Date : 2016-06-26 DOI: 10.1145/2935651.2935660
N. Huy, Gihan Hettiarachchi, Youngki Lee, R. Balan
{"title":"Small Scale Deployment of Seat Occupancy Detectors","authors":"N. Huy, Gihan Hettiarachchi, Youngki Lee, R. Balan","doi":"10.1145/2935651.2935660","DOIUrl":"https://doi.org/10.1145/2935651.2935660","url":null,"abstract":"In this paper, we present the results of a small-scale field deployment of our capacitance-based seat occupancy detector [1]. We deployed our sensors to 36 seats in our university library and measured the performance of our system over a period of 8 weeks. As part of this deployment, we had to tackle numerous real-world deployment issues such as hardware failure, variations in signal quality, and interference caused by multiple objects in near proximity. We present our overall system design, along with the modifications we made to tackle various real-world problems. Finally, we present the results of our deployment which showed that our system achieves a reasonably high level of accuracy at 91.2%.","PeriodicalId":139697,"journal":{"name":"Workshop on Physical Analytics","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123161528","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 7
MyDrive: Drive Behavior Analytics Method And Platform MyDrive:驾驶行为分析方法和平台
Workshop on Physical Analytics Pub Date : 2016-06-26 DOI: 10.1145/2935651.2935652
T. Banerjee, A. Chowdhury, T. Chakravarty
{"title":"MyDrive: Drive Behavior Analytics Method And Platform","authors":"T. Banerjee, A. Chowdhury, T. Chakravarty","doi":"10.1145/2935651.2935652","DOIUrl":"https://doi.org/10.1145/2935651.2935652","url":null,"abstract":"In recent times, research on intelligent transportation and drive quality characterization has emerged to be an important area in the domain of intelligent vehicular telematics. The estimation of driving behavior quality and relative assessment of risky driving has always been a topic of interest for fleet managers, vehicle owners as well as the insurance providers. The most appealing use case that has come up is the analysis and reporting of the driving behavior, so that the drivers can get the feedback and change their driving pattern accordingly. Assessing driving style of an individual, relative categorization in a group of drivers, identifying his abnormal trips among all trips, demands continuous monitoring of the driver. In order to address these problems a statistical aggregate model is required. In this paper we propose an algorithm Skill- Aggression Quantifier (SAQ) which monitors, quantifies and classifies driving styles. The formulated idea has been implemented in an automated tool \"MyDrive\", which monitors and analyses the road-vehicle-driver interaction and models the driving styles of the individuals statistically.","PeriodicalId":139697,"journal":{"name":"Workshop on Physical Analytics","volume":"167 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114802233","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 14
AnnoTainted: Automating Physical Activity Ground Truth Collection Using Smartphones anno污秽:使用智能手机自动收集体力活动的地面真相
Workshop on Physical Analytics Pub Date : 2016-06-26 DOI: 10.1145/2935651.2935653
Rahul Majethia, Akshit Singhal, Lakshmi Manasa K, K. Sahiti, Shubhangi Kishore, Vijay Nandwani
{"title":"AnnoTainted: Automating Physical Activity Ground Truth Collection Using Smartphones","authors":"Rahul Majethia, Akshit Singhal, Lakshmi Manasa K, K. Sahiti, Shubhangi Kishore, Vijay Nandwani","doi":"10.1145/2935651.2935653","DOIUrl":"https://doi.org/10.1145/2935651.2935653","url":null,"abstract":"In this work, we provide motivation for a zero-effort crowdsensing task: auto-annotated ground truth collection for physical activity recognition. Data obtained through Smartphones for classification of human activities is prone to discrepancies, which reiterates the need for better and larger activity datasets. Artificial data generation algorithms fail to efficiently generate quality instances for minority data. In the proposed model, crowd-sourced sensor data is classified by a robust classifier built by researchers ground up. We nominate a Generic Classifier with ≥ 95% accuracy for this purpose. Data collection and distribution models which ensure that the crowd client receives non-skewed, quality data from locations with higher degree of activity occurrence are elucidated upon. Also integrated within our proposed model are Location-Specific Classifiers, which can be utilized by developers to optimize on location-specific tasks. Effective validation of classified activities using diverse sensor data streams improves the proposed classifier systems and boosts ground-truth accuracy.","PeriodicalId":139697,"journal":{"name":"Workshop on Physical Analytics","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123273794","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 2
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